Image-guided radiosurgery and radiotherapy systems (image-guided radiation treatment systems, collectively) are radiation treatment systems that use external radiation beams to treat pathological anatomies (e.g., tumors, lesions, vascular malformations, nerve disorders, etc.) by delivering a prescribed dose of radiation (e.g., x-rays or gamma rays) to the pathological anatomy while minimizing radiation exposure to surrounding tissue and critical anatomical structures (e.g., the spinal cord). Both radiosurgery and radiotherapy are designed to necrotize or damage the pathological anatomy while sparing healthy tissue and the critical structures. Radiotherapy is characterized by a low radiation dose per treatment (1-2 Gray per treatment), and many treatments (e.g., 30 to 45 treatments). Radiosurgery is characterized by a relatively high radiation dose (typically 5 Gray or more per treatment) in one to five treatments (1 Gray equals one joule per kilogram). Image-guided radiosurgery and radiotherapy systems eliminate the need for invasive frame fixation by tracking patient pose (position and orientation) during treatment. In addition, while frame-based systems are generally limited to intracranial therapy, image-guided systems are not so limited.
Image-guided radiotherapy and radiosurgery systems include gantry-based systems and robotic-based systems. In gantry-based systems, a radiation source is attached to a gantry that moves around a center of rotation (isocenter) in a single plane. Each time a radiation beam is delivered during treatment, the axis of the beam passes through the isocenter. Treatment angles are therefore limited by the rotation range of the radiation source and the degrees of freedom of a patient positioning system. In robotic-based systems, the radiation source is not constrained to a single plane of rotation, having five or more degrees of freedom.
In conventional image-guided radiation treatment systems, patient tracking during treatment is accomplished by comparing two-dimensional (2D) in-treatment x-ray images of the patient to 2D digitally reconstructed radiographs (DRRs) derived from the three dimensional (3D) pre-treatment imaging data that is used for diagnosis and treatment planning. The pre-treatment imaging data may be computed tomography (CT) data, magnetic resonance imaging (MRI) data, positron emission tomography (PET) data or 3D rotational angiography (3DRA), for example. Typically, the in-treatment x-ray imaging system is stereoscopic, producing images of the patient from two or more different points of view (e.g., orthogonal), and a corresponding DRR is generated for each point of view. A DRR is a synthetic x-ray image generated by casting (mathematically projecting) rays through a 3D image, simulating the geometry of the in-treatment x-ray imaging system. The resulting DRR then has the same scale and point of view as the in-treatment x-ray imaging system. To generate a DRR, the 3D imaging data is divided into voxels (volume elements) and each voxel is assigned an attenuation (loss) value derived from the 3D imaging data. The relative intensity of each pixel in a DRR is then the summation of the voxel losses for each ray projected through the 3D image. Different patient poses are simulated by performing 3D transformations (rotations and translations) on the 3D imaging data before the DRR is generated. The 3D transformation and DRR generation may be performed iteratively in real time, during treatment, or alternatively, the DRRs (in each projection) corresponding to an expected range of patient poses may be pre-computed before treatment begins.
Each comparison of an in-treatment x-ray image with a DRR produces a similarity measure or, equivalently, a difference measure (e.g., cross correlation, entropy, mutual information, gradient correlation, pattern intensity, gradient difference, image intensity gradients) that can be used to search for a 3D transformation that produces a DRR with a higher similarity measure to the in-treatment x-ray image (or to search directly for a pre-computed DRR as described above). When the similarity measure is sufficiently maximized (or equivalently, a difference measure is minimized), the 3D transformation corresponding to the DRR can be used to align the 3D coordinate system of the treatment plan with the 3D coordinate system of the treatment delivery system, to conform the relative positions of the radiation source and the patient to the treatment plan. In the case of pre-computed DRRs, the maximum similarity measure may be used to compute a differential 3D transformation between the two closest DRRs. FIG. 1 illustrates the process described above for the case of in-treatment DRR generation.
One limiting factor in the accuracy of the registration and tracking algorithms is that bony structures, such as a spinal structure, may partially or completely block the tumor in one or more of the projections, reducing the visibility of the tumor in the corresponding projection in the in-treatment x-ray images.